Jeroen J. Jansen

Stress‐induced DNA methylation changes and their heritability in asexual dandelions

*DNA methylation can cause heritable phenotypic modifications in the absence of changes in DNA sequence. Environmental stresses can trigger methylation changes and this may have evolutionary consequences, even in the absence of sequence variation. However, it remains largely unknown to what extent environmentally induced methylation changes are transmitted to offspring, and whether observed methylation variation is truly independent or a downstream consequence of genetic variation between individuals. *Genetically identical apomictic dandelion (Taraxacum officinale) plants were exposed to different ecological stresses, and apomictic offspring were raised in a common unstressed environment. We used methylation-sensitive amplified fragment length polymorphism markers to screen genome-wide methylation alterations triggered by stress treatments and to assess the heritability of induced changes. *Various stresses, most notably chemical induction of herbivore and pathogen defenses, triggered considerable methylation variation throughout the genome. Many modifications were faithfully transmitted to offspring. Stresses caused some epigenetic divergence between treatment and controls, but also increased epigenetic variation among plants within treatments. *These results show the following. First, stress-induced methylation changes are common and are mostly heritable. Second, sequence-independent, autonomous methylation variation is readily generated. This highlights the potential of epigenetic inheritance to play an independent role in evolutionary processes, which is superimposed on the system of genetic inheritance.

Birds exploit herbivore-induced plant volatiles to locate herbivorous prey

Arthropod herbivory induces plant volatiles that can be used by natural enemies of the herbivores to find their prey. This has been studied mainly for arthropods that prey upon or parasitise herbivorous arthropods but rarely for insectivorous birds, one of the main groups of predators of herbivorous insects such as lepidopteran larvae. Here, we show that great tits (Parus major) discriminate between caterpillar-infested and uninfested trees. Birds were attracted to infested trees, even when they could not see the larvae or their feeding damage. We furthermore show that infested and uninfested trees differ in volatile emissions and visual characteristics. Finally, we show, for the first time, that birds smell which tree is infested with their prey based on differences in volatile profiles emitted by infested and uninfested trees. Volatiles emitted by plants in response to herbivory by lepidopteran larvae thus not only attract predatory insects but also vertebrate predators.

Plants Know Where It Hurts: Root and Shoot Jasmonic Acid Induction Elicit Differential Responses in Brassica oleracea

Plants respond to herbivore attack by rapidly inducing defenses that are mainly regulated by jasmonic acid (JA). Due to the systemic nature of induced defenses, attack by root herbivores can also result in a shoot response and vice versa, causing interactions between above- and belowground herbivores. However, little is known about the molecular mechanisms underlying these interactions. We investigated whether plants respond differently when roots or shoots are induced. We mimicked herbivore attack by applying JA to the roots or shoots of Brassica oleracea and analyzed molecular and chemical responses in both organs. In shoots, an immediate and massive change in primary and secondary metabolism was observed. In roots, the JA-induced response was less extensive and qualitatively different from that in the shoots. Strikingly, in both roots and shoots we also observed differential responses in primary metabolism, development as well as defense specific traits depending on whether the JA induction had been below- or aboveground. We conclude that the JA response is not only tissue-specific but also dependent on the organ that was induced. Already very early in the JA signaling pathway the differential response was observed. This indicates that both organs have a different JA signaling cascade, and that the signal eliciting systemic responses contains information about the site of induction, thus providing plants with a mechanism to tailor their responses specifically to the organ that is damaged.

ACPA fine-specificity profiles in early rheumatoid arthritis patients do not correlate with clinical features at baseline or with disease progression.

IntroductionAutoantibodies against citrullinated peptides/proteins (ACPA) are found in approximately 75% of the sera of patients with rheumatoid arthritis (RA). The RA-specific ACPA are frequently present prior to disease onset and their presence associates with a more erosive disease course. ACPA can therefore be used to aid the diagnosis and prognosis of RA. Recently, it became clear that ACPA are very heterogeneous, both in an individual patient and among different patients. The aim of this study was to investigate whether clinically meaningful ACPA profiles exist in early RA patients.MethodsTwenty citrullinated peptides and the corresponding non-citrullinated control peptides were immobilized on microarray sensor chips. Sera from 374 early arthritis patients were analyzed by surface plasmon resonance imaging (i SPR) of biomolecular interactions on the sensor chip.ResultsCluster analysis of the reactivities with the citrullinated peptides, after subtraction of the reactivities with the corresponding control peptides confirmed the heterogeneity of the ACPA response in RA and revealed 12 distinct ACPA profiles. The association of the 5 most frequent profiles with clinical features at diagnosis and during the disease course was examined, showing no statistically significant associations.ConclusionsCompared to the detection of ACPA in RA sera by CCP-based assays, ACPA profiling in early arthritis patients did not reveal associations with disease activity and progression scores.

Novel chemistry of invasive plants: exotic species have more unique metabolomic profiles than native congeners

It is often assumed that exotic plants can become invasive when they possess novel secondary chemistry compared with native plants in the introduced range. Using untargeted metabolomic fingerprinting, we compared a broad range of metabolites of six successful exotic plant species and their native congeners of the family Asteraceae. Our results showed that plant chemistry is highly species-specific and diverse among both exotic and native species. Nonetheless, the exotic species had on average a higher total number of metabolites and more species-unique metabolites compared with their native congeners. Herbivory led to an overall increase in metabolites in all plant species. Generalist herbivore performance was lower on most of the exotic species compared with the native species. We conclude that high chemical diversity and large phytochemical uniqueness of the exotic species could be indicative of biological invasion potential.

Identification of Pseudomonas aeruginosa and Aspergillus fumigatus mono- and co-cultures based on volatile biomarker combinations

Volatile organic compound (VOC) analysis in exhaled breath is proposed as a non-invasive method to detect respiratory infections in cystic fibrosis patients. Since polymicrobial infections are common, we assessed whether we could distinguish Pseudomonas aeruginosa and Aspergillus fumigatus mono- and co-cultures using the VOC emissions. We took headspace samples of P. aeruginosa, A. fumigatus and co-cultures at 16, 24 and 48 h after inoculation, in which VOCs were identified by thermal desorption combined with gas chromatography - mass spectrometry. Using multivariate analysis by Partial Least Squares Discriminant Analysis we found distinct VOC biomarker combinations for mono- and co-cultures at each sampling time point, showing that there is an interaction between the two pathogens, with P. aeruginosa dominating the co-culture at 48 h. Furthermore, time-independent VOC biomarker combinations were also obtained to predict correct identification of P. aeruginosa and A. fumigatus in mono-culture and in co-culture. This study shows that the VOC combinations in P. aeruginosa and A. fumigatus co-microbial environment are different from those released by these pathogens in mono-culture. Using advanced data analysis techniques such as PLS-DA, time-independent pathogen specific biomarker combinations can be generated that may help to detect mixed respiratory infections in exhaled breath of cystic fibrosis patients.

An ecogenomic analysis of herbivore-induced plant volatiles in Brassica juncea

Upon herbivore feeding, plants emit complex bouquets of induced volatiles that may repel insect herbivores as well as attract parasitoids or predators. Due to differences in the temporal dynamics of individual components, the composition of the herbivore‐induced plant volatile (HIPV) blend changes with time. Consequently, the response of insects associated with plants is not constant either. Using Brassica juncea as the model plant and generalist Spodoptera spp. larvae as the inducing herbivore, we investigated herbivore and parasitoid preference as well as the molecular mechanisms behind the temporal dynamics in HIPV emissions at 24, 48 and 72 h after damage. In choice tests, Spodoptera litura moth preferred undamaged plants, whereas its parasitoid Cotesia marginiventris favoured plants induced for 48 h. In contrast, the specialist Plutella xylostella and its parasitoid C. vestalis preferred plants induced for 72 h. These preferences matched the dynamic changes in HIPV blends over time. Gene expression analysis suggested that the induced response after Spodoptera feeding is mainly controlled by the jasmonic acid pathway in both damaged and systemic leaves. Several genes involved in sulphide and green leaf volatile synthesis were clearly up‐regulated. This study thus shows that HIPV blends vary considerably over a short period of time, and these changes are actively regulated at the gene expression level. Moreover, temporal changes in HIPVs elicit differential preferences of herbivores and their natural enemies. We argue that the temporal dynamics of HIPVs may play a key role in shaping the response of insects associated with plants.

Chemometrics in analytical chemistry—part I: history, experimental design and data analysis tools

Chemometrics has achieved major recognition and progress in the analytical chemistry field. In the first part of this tutorial, major achievements and contributions of chemometrics to some of the more important stages of the analytical process, like experimental design, sampling, and data analysis (including data pretreatment and fusion), are summarised. The tutorial is intended to give a general updated overview of the chemometrics field to further contribute to its dissemination and promotion in analytical chemistry.

Editorial–data analysis in metabolomics

The chemical diversity and the precision by which we are able to observe metabolism has taken a step change with the advent of metabolomics technology. Although the end goal of covering the entire metabolome by one chemical analysis is still far away, and indeed may never be realised considering the immense chemical diversity of metabolites, an ever-growing number of metabolites can be analysed in ever-lower concentrations. Also, analysis times become ever-shorter which dramatically increases throughput and therefore the number of samples a metabolomic study can contain. To battle the resulting flood of data coming towards the biologist, researchers that employ metabolomics often turn to ‘chemometrics’, the research field that works on the representation of data from complex chemical analyses in simplified, interpretable models. The complexity of the biological and chemical background poses the chemometrician with a series of novel challenges. First of all, metabolomics is mostly used to gain insight in the biological system with highly specific underlying concepts while these standard methods provide a very generic view on the metabolism. Secondly, the latest developments in metabolomics also provide purely chemometric novel challenges: for example Nuclear Magnetic Resonance spectroscopy is used in metabolomics in a completely different fashion than in earlier applications, such as purity analysis in organic synthesis and protein structure elucidation. Furthermore, the metabolomic platforms themselves are pushing the limits of analytical chemical technology. Knowing, understanding and accounting for their (mis)behaviour in the large sample sets is also essential to make metabolomics a widely accepted systems-biological technology. We have made it our task as guest editors to provide you with the newest developments in data analysis in metabolomics and to show to which lengths biological knowledge can be brought with this. All aforementioned aspects will be addressed in the manuscripts we selected for publication. One of the drawbacks of data analysis is that it is generally highly technology-driven and therefore requires considerable programming skills. The contribution of Sun et al. describes their new toolbox COVAIN for many of the standard data analysis methods in current use for metabolomics, as well as many more advanced techniques. However, the toolbox brings them closer to the non-specialist researcher because it comes fitted with an easy-to-use graphical user interface. Also the contribution of Lei et al. covers new software: a new version of the package MET-IDEA for metabolomics data handling and processing. This program will considerably tighten the gap between the laboratory bench on which the mass spectrometer stands and the computer screen that depicts the data. The paper by Jankevics et al. is dedicated to this in silico screen and describes a method to determine the integrity and quality of the collected mass spectrometric data: applying this method should lead to more robust and information-dense metabolic profiles fit for subsequent data analysis. Several other contributions focus on the models used for this data analysis, to provide results more focused upon the experimental factors relevant to the studied biological concepts. Koekemoer et al. show that one PCA model may not be able to cover all subtleties in different treatment groups and propose a method to model these and recover them in one large PCA model. The paper by Lemanska et al. shows how they were able pinpoint the effect oral rinse has on the metabolic composition of saliva with Analysis of Variance coupled to Simultaneous Component Analysis (ASCA), which is one of the methods developed in metabolomics to target a multivariate analysis more towards the biologically relevant information collected in the experiment. Xu and Goodacre explore the use of Consensus PCA to analyze bifactorial experimental data and show that it gives similar results to ANOVA-PCA while outperforming standard PCA analysis. A final challenge in multivariate analysis is to determine significance of experimental factors. For example in PLS-DA this can be done in several ways. Szymanska et al. compare several methods for this in their contribution and show which statistic measures are most powerful for model diagnostics. Finally, two contributions explore new signatures of metabolic variation to indicate system function. Jansen et al. propose to not only look at differences between treatment groups, but also at individual differences in metabolism and how they may be related to the experiment. The paper by Daykin et al. proposes to look beyond the small organic molecules freely dissolved in the biofluids, into the metabolites attached to macromolecules such as proteins. The specific NMR platforms used to collect the data to reveal such chemical interaction generate highly complex but well-structured data, which is subsequently used in the data analysis. We feel the selected contributions not only show that data analysis is highly essential in all aspects of the metabolomics pipeline, but that this overview also shows that metabolomics is a truly interdisciplinary field in which the borders between biology, analytical chemistry and data analysis vanish more and more.

Diagnosing eosinophilic asthma using a multivariate prediction model based on blood granulocyte responsiveness

The identification of inflammatory asthma phenotypes, using sputum analysis, has proven its value in diagnosis and disease monitoring. However due to technical limitations of sputum analysis, there is a strong need for fast and noninvasive diagnostics. This study included the activation state of eosinophils and neutrophils in peripheral blood to phenotype and monitor asthma.